Diseño y desarrollo de una arquitectura software genérica orientada a servicios para la construcción de un middleware grid orientado a la gestión y proceso seguro de información en formato DICOM sobre un marco ontológico

Una de las áreas que más se ha beneficiado del soporte digital es la imagen médica, reforzada por la aparición del estándar Digital Imaging and Communication in Medicine (DICOM), evolucionado a lo largo de los años para soportar no sólo imágenes médicas sino otros tipos de información médica como videos, señales e incluso informes estructurados (DICOM-SR). Con la aparición de DICOM, se ha conseguido la integración de dispositivos de adquisición, visualización, impresión y almacenamiento de imágenes médicas de diferentes fabricantes, al ser este un estándar utilizado por todas las compañias proveedoras. En la actualidad, los sistemas que trabajan con imagen médica digital, como PACS, RIS y HIS permiten integrar los datos a nivel departamental y hospitalario, existiendo soluciones comerciales muy efectivas. En estos sistemas, la seguridad de usuarios y datos se gestiona en un único dominio administrativo restringido. A consecuencia de su uso en producción en la práctica clínica, existe en la actualidad una gran cantidad de información en formato DICOM cuya utilización se restringe generalmente al tratamiento de los pacientes individuales. Sin embargo, la investigación médica requiere consolidar información multicéntrica para la extracción de patrones y la validación de técnicas y diagnósticos, realizándose esta actividad de forma manual y sin herramientas especiales. En la presente tesis se plantea como objetivo general la definición de una Arquitectura Orientada a Servicios (SOA) y la implementación de un Middleware Grid basado en esta arquitectura, cuya principal función será la gestión, integración y proceso de información en formato DICOM almacenada de forma distribuida en diferentes dominios administrativos, de forma segura y estructurada semánticamente mediante la definición de ontologías médicas basadas en el informe estructurado y los estudios DICOM. Este middleware, proporciona a los desarrolladores un interfaz de alto nivel orientado a objetos que permite aumentar la productividad en el desarrollo de aplicaciones en diferentes ámbitos médicos. Las aportaciones más destacables de esta tesis son las siguientes: " Diseño de una Arquitectura Grid de propósito general para virtualizar el almacenamiento distribuido y proceso de datos en formato DICOM, basada en Web Services Resorce Framework (WSRF), frente a otras arquitecturas existentes que no utilizan estándares. " Desarrollo de un modelo para la indexación y estructuración de datos DICOM basado en ontologías médicas obtenidas a partir de informes radiológicos, frente a los modelos convencionales basados en nombres identificadores y metadata básica. " Diseño e implementación de un sistema de autorización que estructura los permisos de los miembros de las organizaciones virtuales a partir de ontologías médicas. " Implementación de una plataforma y de los objetos de alto nivel necesarios, junto con diversas aplicaciones para la asistencia en la investigación en Diagnóstico por Imagen.Segrelles Quilis, JD. (2008). Diseño y desarrollo de una arquitectura software genérica orientada a servicios para la construcción de un middleware grid orientado a la gestión y proceso seguro de información en formato DICOM sobre un marco ontológico [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1973Palanci

Panel web de gestión automatizada para actividades educativas no presenciales

Esta contribución presenta un recurso docente para automatizar la gestión de actividades educativas no presenciales, que involucren laboratorios computacionales remotos de prácticas. La interacción profesor-alumno es especialmente necesaria en actividades no presenciales, como es el caso de cursos online asíncronos y asignaturas con dispensa de asistencia. Por ello, el panel web permite: i) el envío periódico de mensajes de correo electrónico personalizados para los alumnos; ii) la gestión centralizada de las credenciales de alumnos para los laboratorios remotos; iii) disponer de una visión actualizada del ciclo de vida de los alumnos (alumnos concurrentes, tiempo restante, etc.). Se describe el uso de la herramienta en el contexto de un curso online asíncrono que opera a escala mundial así como su extensión a asignaturas con dispensa de asistencia. El panel web, creado con Google Spreadsheets y liberado bajo licencia Creative Commons, ha permitido la gestión de más de 350 alumnos, automatizar el envío de más de 1000 mensajes personalizados y facilitar las labores de gestión de credenciales de dichas actividades educativas, pudiendo ser fácilmente adaptado a otras actividades educativas afines.Los autores quieren agradecer al Vicerrectorado de Estudios, Calidad y Acreditación de la UPV por la financiación del proyecto PIME “Análisis y Evaluación de Impacto del Cloud Computing en la Gestión de entornos Virtuales Computacionales en la Enseñanza", con referencia (A014). GM quiere agradecer a l’EscolaTècnica Superior d’Enginyeria Informàtica de la Universitat Politècnica de València el soporte económico para la presentación de este trabajo.Moltó Martínez, G.; Segrelles Quilis, JD. (2016). Panel web de gestión automatizada para actividades educativas no presenciales. Editorial Universidad de Almería. http://hdl.handle.net/10251/78305

A Pilot Experience with Software Programming Environments as a Service for Teaching Activities

[EN] Software programming is one of the key abilities for the development of Computational Thinking (CT) skills in Science, Technology, Engineering and Mathematics (STEM). However, specific software tools to emulate realistic scenarios are required for effective teaching. Unfortunately, these tools have some limitations in educational environments due to the need of an adequate configuration and orchestration, which usually assumes an unaffordable work overload for teachers and is inaccessible for students outside the laboratories. To mitigate the aforementioned limitations, we rely on cloud solutions that automate the process of orchestration and configuration of software tools on top of cloud computing infrastructures. This way, the paper presents ACTaaS as a cloud-based educational resource that deploys and orchestrates a whole realistic software programming environment. ACTaaS provides a simple, fast and automatic way to set up a professional integrated environment without involving an overload to the teacher, and it provides an ubiquitous access to the environment. The solution has been tested in a pilot group of 28 students. Currently, there is no tool like ACTaaS that allows such a high grade of automation for the deployment of software production environments focused on educational activities supporting a wide range of cloud providers. Preliminary results through a pilot group predict its effectiveness due to the efficiency to set up a class environment in minutes without overloading the teachers, and providing ubiquitous access to students. In addition, the first student opinions about the experience were greatly positive.This research was funded by Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" grant number AICO/2019/313, and the Vicerrectorado de Estudios, Calidad y Acreditacion of the Universitat Politecnica de Valencia grant number PIME/19-20/166.Calatrava Arroyo, A.; Ramos Montes, M.; Segrelles Quilis, JD. (2021). A Pilot Experience with Software Programming Environments as a Service for Teaching Activities. Applied Sciences. 11(1). https://doi.org/10.3390/app11010341S111Campbell, J. O., Bourne, J. R., Mosterman, P. J., & Brodersen, A. J. (2002). The Effectiveness of Learning Simulations for Electronic Laboratories. Journal of Engineering Education, 91(1), 81-87. doi:10.1002/j.2168-9830.2002.tb00675.xFraser, D. M., Pillay, R., Tjatindi, L., & Case, J. M. (2007). Enhancing the Learning of Fluid Mechanics Using Computer Simulations. Journal of Engineering Education, 96(4), 381-388. doi:10.1002/j.2168-9830.2007.tb00946.xTroussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698. doi:10.1016/j.compedu.2019.103698González-Martínez, J. A., Bote-Lorenzo, M. L., Gómez-Sánchez, E., & Cano-Parra, R. (2015). Cloud computing and education: A state-of-the-art survey. Computers & Education, 80, 132-151. doi:10.1016/j.compedu.2014.08.017Moreno, A. M., Sanchez-Segura, M.-I., Medina-Dominguez, F., & Carvajal, L. (2012). Balancing software engineering education and industrial needs. Journal of Systems and Software, 85(7), 1607-1620. doi:10.1016/j.jss.2012.01.060Desai, C., Janzen, D., & Savage, K. (2008). A survey of evidence for test-driven development in academia. ACM SIGCSE Bulletin, 40(2), 97-101. doi:10.1145/1383602.1383644Barriocanal, E. G., Urbán, M.-Á. S., Cuevas, I. A., & Pérez, P. D. (2002). An experience in integrating automated unit testing practices in an introductory programming course. ACM SIGCSE Bulletin, 34(4), 125-128. doi:10.1145/820127.820183OASIS Topology and Orchestration Specification for Cloud Applications (TOSCA) https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=toscaTomarchio, O., Calcaterra, D., & Modica, G. D. (2020). Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. Journal of Cloud Computing, 9(1). doi:10.1186/s13677-020-00194-7Cloudify https://cloudify.coStarCluster http://web.mit.edu/stardev/cluster/ElastiCluster https://elasticluster.github.io/elasticluster/Apache ARIA TOSCA Orchestration Engine http://ariatosca.incubator.apache.orgOpenTOSCA http://www.opentosca.orgGiannakopoulos, I., Papailiou, N., Mantas, C., Konstantinou, I., Tsoumakos, D., & Koziris, N. (2014). CELAR: Automated application elasticity platform. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004481Yangui, S., Marshall, I.-J., Laisne, J.-P., & Tata, S. (2013). CompatibleOne: The Open Source Cloud Broker. Journal of Grid Computing, 12(1), 93-109. doi:10.1007/s10723-013-9285-0Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5Ansible https://www.ansible.com/JUnit Framework for Java https://junit.org/Check Unit Testing Framework for C https://libcheck.github.io/check

An OGSA Middleware for Managing Medical Images Using Ontologies

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10877-005-0675-0This article presents a Middleware based on Grid Technologies that addresses the problem of sharing, transferring and processing DICOM medical images in a distributed environment using an ontological schema to create virtual communities and to define common targets. It defines a distributed storage that builds-up virtual repositories integrating different individual image repositories providing global searching, progressive transmission, automatic encryption and pseudo-anonimisation and a link to remote processing services. Users from a Virtual Organisation can share the cases that are relevant for their communities or research areas, epidemiological studies or even deeper analysis of complex individual cases. Software architecture has been defined for solving the problems that has been exposed before. Briefly, the architecture comprises five layers (from the more physical layer to the more logical layer) based in Grid Thecnologies. The lowest level layers (Core Middleware Layer and Server Services layer) are composed of Grid Services that implement the global managing of resources. The Middleware Components Layer provides a transparent view of the Grid environment and it has been the main objective of this work. Finally, the upest layer (the Application Layer) comprises the applications, and a simple application has been implemented for testing the components developed in the Components Middleware Layer. Other side-results of this work are the services developed in the Middleware Components Layer for managing DICOM images, creating virtual DICOM storages, progressive transmission, automatic encryption and pseudo-anonimisation depending on the ontologies. Other results, such as the Grid Services developed in the lowest layers, are also described in this article. Finally a brief performance analysis and several snapshots from the applications developed are shown. The performance analysis proves that the components developed in this work provide image processing applications with new possibilities for large-scale sharing, management and processing of DICOM images. The results show that the components fulfil the objectives proposed. The extensibility of the system is achieved by the use of open methods and protocols, so new components can be easily added.Blanquer Espert, I.; Hernández García, V.; Segrelles Quilis, JD. (2005). An OGSA Middleware for Managing Medical Images Using Ontologies. Journal of Clinical Monitoring and Computing. 19:295-305. doi:10.1007/s10877-005-0675-0S29530519“European DataGrid Project”. http://www.eu-datagrid.org.“Biomedical Informatics Research”. http://www.nbirn.net/.“ACI project MEDIGRID: medical data storage and processing on the GRID”.http://www.creatis.insa-lyon.fr/MEDIGRID/.“Information eXtraction from Images (IXI) Grid Services for Medical Imaging”. Working Notes of the Workshop on Distributed Databases and processing in Medical Image Computing (DIDAMIC'04). Pag 65.“NeuroBase: Management of Distributed and Heterogeneous Information Sources in Neuroimaging”. Working Notes of the Workshop on Distributed Databases and processing in Medical Image Computing (DIDAMIC'04). Pag 85.Digital Imaging and Communications in Medicine (DICOM) Part 10: Media Storage and File Format for Media Interchange. National Electrical Manufacturers Association, 1300 N. 17th Street, Rosslyn, Virginia 22209 USA.“Open Grid Services Architecture (OGSA)”, http://www.globus.org/ogsa.Globus alliance Home Page. “Relevant documents”, http://www.globus.orgAllen Wyke R, Watt A, “XML Schema Essentials”. Wiley Computer Pub. ISBN 0-471-412597Web security and commerce/Simson Garfinkel. - Cambridge: O'Reilly, 1997. - 483 p.; 23 cm. ISBN 1565922697“The GridFTP Protocol and Software”. http://www-fp.globus.org/datagrid/gridftp.html.JPEG2000: Image compression fundamentals, standards and practice/David S. Taubman, Michael W. Marcellin. – Boston [etc.] : Kluwer Academic, cop. 2002. - XIX, 773 p.; 24 cm. + 1 CD-Rom - (The Kluwer international series in engineering and computer science) ISBN 079237519XBradley J, Erickson MD, “Irreversible Compression of Medical Images”, Dpt. Radiology, Mayo F., Rochester, MN, Jo. of D. Imaging, DOI: 10.1007/s10278-002-0001-z, 02.Monitoring & Discovery System (MDS)” http://www-unix.globus.org/toolkit/mds/“Key management for encrypted data storage in distributed systems”. Proceedings of HeathGrid 2004

Experiencia en informática: uso del portafolio como herramienta para la mejora de la motivación en el aprendizaje de los alumnos

El rediseño de las guías docentes para su adecuación a la filosofía del Espacio Europeo de Educación Superior (EEES), ha tenido como consecuencia el aumento del empleo de metodologías activas y nuevas estrategias basadas en Evaluaciones Continuas Formativas (ECF) en los procesos de enseñanzaaprendizaje. En este marco, el uso del portafolio y las ECF son habituales en las nuevas guías docentes. El portafolio docente permite al profesor evaluar los esfuerzos realizados por el alumno y su progreso, y al alumno documentar sus reflexiones sobre el progreso y nivel alcanzado en los resultados de aprendizaje (RA) planteados. La ECF permite evaluar el nivel real alcanzado respecto a los RA. Este trabajo combina la aplicación del portafolio docente y una ECF, con el objeto de medir el grado de motivación del alumno de una forma continua a lo largo del curso, a través de la recogida de los esfuerzos y reflexiones realizadas por el alumno y del nivel realmente alcanzado a lo largo del curso respecto a las RA. La experiencia docente se ha aplicado en la asignatura de Informática, Grado de Ingeniería Electrónica y Automática (GEIA) de la Escuela Técnica Superior en Ingenieria del Diseño (ETSID) de la Universitat Politècnica de València (UPV). La conclusión obtenida determina que la combinación de una ECF y el uso del portafolio permite evaluar la motivación de los alumnos de forma continua a lo largo del curso, permitiendo mejorarla a través de nuevas innovaciones en aquellos puntos que se detecta una disminución. Además, se observa que se ha conseguido una estabilidad en la motivación de los alumnos a lo largo del curso.SUMMARY -- The redesign of the teaching guides for its adaptation to the philosophy of the European Higher Education Area (EHEA), has led to the expansion of new active methodologies and evaluation strategies based on Continuous Assessments Formative (CAFs) in the process of teaching and learning. In this framework, the use of portfolio and CAF are regulars in the new educational guides. The portfolio allows the teacher to assess the efforts of the students and their progress, and document their reflections on student progress and level achieved in Learning Outcomes (LOs) raised. The CAF allows assessing the real level reached on each LOs. This work combine the application of the portfolio and a ECF, through an educational experience, in order to measure the degree of student motivation in a continuous Manner in the course, through the collection of efforts and reflexions by the student and their level actually achieved over the course regarding LOs. The teaching experience has been applied in the materia of Computer Science, in the Electronics and Control Engineering (GEIA) degree of the School of Design in Engineering (ETSID) of the Technical University of Valencia (UPV). The conclusion determines that the combination of a CAF and the portfolio to evaluate the students' motivation continuously throughout the course, allowing improve this through new innovations on points that the motivation fall is detected. Furthermore, it appears that it has gotten stability in motivating students throughout the course

Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

[EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.This research was funded by the Spanish "Ministerio de Economia, Industria y Competitividad through grant number TIN2016-79951-R (BigCLOE)", the "Vicerrectorado de Estudios, Calidad y Acreditacion" of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29 and PIME/19-20/166, and by the Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" with reference number AICO/2019/313.Moltó, G.; Naranjo-Delgado, DM.; Segrelles Quilis, JD. (2020). Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS. Applied Sciences. 10(24):1-13. https://doi.org/10.3390/app10249148S1131024Motiwalla, L., Deokar, A. V., Sarnikar, S., & Dimoka, A. (2019). Leveraging Data Analytics for Behavioral Research. Information Systems Frontiers, 21(4), 735-742. doi:10.1007/s10796-019-09928-8Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330661Blikstein, P. (2013). Multimodal learning analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK ’13. doi:10.1145/2460296.2460316Hewson, E. R. F. (2018). Students’ Emotional Engagement, Motivation and Behaviour Over the Life of an Online Course: Reflections on Two Market Research Case Studies. Journal of Interactive Media in Education, 2018(1). doi:10.5334/jime.472Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of Participant Behavior in a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 18(6). doi:10.19173/irrodl.v18i6.3087Cross, S., & Whitelock, D. (2016). Similarity and difference in fee-paying and no-fee learner expectations, interaction and reaction to learning in a massive open online course. Interactive Learning Environments, 25(4), 439-451. doi:10.1080/10494820.2016.1138312Charleer, S., Klerkx, J., & Duval, E. (2014). Learning Dashboards. Journal of Learning Analytics, 1(3), 199-202. doi:10.18608/jla.2014.13.22Worsley, M. (2012). Multimodal learning analytics. Proceedings of the 14th ACM international conference on Multimodal interaction - ICMI ’12. doi:10.1145/2388676.2388755Spikol, D., Prieto, L. P., Rodríguez-Triana, M. J., Worsley, M., Ochoa, X., Cukurova, M., … Ringtved, U. L. (2017). Current and future multimodal learning analytics data challenges. Proceedings of the Seventh International Learning Analytics & Knowledge Conference. doi:10.1145/3027385.3029437Ochoa, X., Worsley, M., Weibel, N., & Oviatt, S. (2016). Multimodal learning analytics data challenges. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16. doi:10.1145/2883851.2883913Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., & Chamba-Eras, L. (2017). Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society, 17(4), 693-709. doi:10.1007/s10209-017-0525-0Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234. doi:10.1080/10494820.2016.1278391Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): a reflective summary of ongoing research and its challenges. Journal of Computer Assisted Learning, 32(3), 281-290. doi:10.1111/jcal.12135Ruiperez-Valiente, J. A., Munoz-Merino, P. J., Gascon-Pinedo, J. A., & Kloos, C. D. (2017). Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX. IEEE Transactions on Human-Machine Systems, 47(6), 909-914. doi:10.1109/thms.2016.2630420Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I., & Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: a MOOC case study. Interactive Learning Environments, 27(5-6), 685-698. doi:10.1080/10494820.2019.1610455Tabaa, Y., & Medouri, A. (2013). LASyM: A Learning Analytics System for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5). doi:10.14569/ijacsa.2013.040516Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92, 578-588. doi:10.1016/j.chb.2018.07.002Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-1078. doi:10.1002/cae.21844Logglyhttps://www.loggly.com/Molto, G., & Caballer, M. (2014). On using the cloud to support online courses. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. doi:10.1109/fie.2014.7044041Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5AWS CloudTrailhttps://aws.amazon.com/cloudtrail/Amazon Simple Storage Service (Amazon S3)http://aws.amazon.com/s3/Naranjo, D. M., Prieto, J. R., Moltó, G., & Calatrava, A. (2019). A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing. Sensors, 19(13), 2952. doi:10.3390/s19132952Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., … Suter, P. (2017). Serverless Computing: Current Trends and Open Problems. Research Advances in Cloud Computing, 1-20. doi:10.1007/978-981-10-5026-8_1Zimmerman, D. W. (1987). Comparative Power of StudentTTest and Mann-WhitneyUTest for Unequal Sample Sizes and Variances. The Journal of Experimental Education, 55(3), 171-174. doi:10.1080/00220973.1987.10806451Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583-621. doi:10.1080/01621459.1952.10483441Voyer, D., & Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174-1204. doi:10.1037/a0036620Ellemers, N., Heuvel, H., Gilder, D., Maass, A., & Bonvini, A. (2004). The underrepresentation of women in science: Differential commitment or the queen bee syndrome? British Journal of Social Psychology, 43(3), 315-338. doi:10.1348/0144666042037999Sheard, M. (2009). Hardiness commitment, gender, and age differentiate university academic performance. British Journal of Educational Psychology, 79(1), 189-204. doi:10.1348/000709908x30440

Virtualized Computational Environments on the cloud to foster group skills through PBL: A case study in architecture

The ODISEA platform provides Virtualized Computational Environments (VCEs) on cloud providers as the computational infrastructure to support educational activities. A VCE consists of a collection of one or more Virtual Machines (VMs) to which the students connect from their own computers. In this paper a case study is presented in the architecture domain where a PBL activity is carried out in working groups. The study involves 293 students organized in 28 pilot groups that use customized VCEs created and deployed through the ODISEA platform on a Cloud, and 30 traditional groups that use a LMS platform. The VCE provides the software, hardware and specific configuration to ease the interrelation and cooperative work between the working groups, enhancing the process tracking and feedback gathering as well as providing a better organization of the teaching material. The results demonstrate that the VCE allows to improve the cooperative work, improving the final marks in the PBL developed by the pilot working groups. Also, an economical study is presented, highlighting the economic benefits of the Cloud with respect to raditional physical laboratories of PCs.The authors wish to thank the financial support received from Vicerrectorado de Estudios, Calidad y Acreditacion of the Universitat Politecnica de Valencia (UPV) to develop the PIME project "Entomos Virtuales Computacionales para la Evaluation de Competencias Transversales en la Nube", with reference A04 and to the Ministerio de Economia, Industria y Cornpetitividad for the project BigCLOE (TIN2016-79951-R). GM would like to thank AWS for the AWS Educate program that provides the credits required to support the educational activities.Segrelles Quilis, JD.; Martínez Antón, A.; Castilla-Cabanes, N.; Moltó, G. (2017). Virtualized Computational Environments on the cloud to foster group skills through PBL: A case study in architecture. Computers and Education. 108:131-144. https://doi.org/10.1016/j.compedu.2017.02.001S13114410

APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools

[EN] Background Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field. Objectives The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers. Methods This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users. Results To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction. Conclusion APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.This study was supported by the program "Ayudas para la contratación de personal investigador en formación de carácter predoctoral, programa VALi+d" under grant number ACIF/2018/148 from the Conselleria d'Educació of the Generalitat Valenciana and the "Fondo Social Europeo" (FSE). The authors would like to thank the Spanish "Ministerio de Economía, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R and the European Commission, Horizon 2020 grant agreement No 826494 (PRIMAGE). The MRI prostate study case used in this article has been retrospectively collected from a project of prostate MRI biomarkers validation.Giménez-Alventosa, V.; Segrelles Quilis, JD.; Moltó, G.; Roca-Sogorb, M. (2020). APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools. Methods of Information in Medicine. 59(S 02):e33-e45. https://doi.org/10.1055/s-0040-1712460Se33e4559S 02Donoho, D. L., Maleki, A., Rahman, I. U., Shahram, M., & Stodden, V. (2009). Reproducible Research in Computational Harmonic Analysis. Computing in Science & Engineering, 11(1), 8-18. doi:10.1109/mcse.2009.15Freedman, L. P., Cockburn, I. M., & Simcoe, T. S. (2015). The Economics of Reproducibility in Preclinical Research. PLOS Biology, 13(6), e1002165. doi:10.1371/journal.pbio.1002165Chillarón, M., Vidal, V., & Verdú, G. (2020). CT image reconstruction with SuiteSparseQR factorization package. Radiation Physics and Chemistry, 167, 108289. doi:10.1016/j.radphyschem.2019.04.039Reader, A. J., Ally, S., Bakatselos, F., Manavaki, R., Walledge, R. J., Jeavons, A. P., … Zweit, J. (2002). One-pass list-mode EM algorithm for high-resolution 3-D PET image reconstruction into large arrays. IEEE Transactions on Nuclear Science, 49(3), 693-699. doi:10.1109/tns.2002.1039550Giménez-Alventosa, V., Antunes, P. C. G., Vijande, J., Ballester, F., Pérez-Calatayud, J., & Andreo, P. (2016). Collision-kerma conversion between dose-to-tissue and dose-to-water by photon energy-fluence corrections in low-energy brachytherapy. Physics in Medicine and Biology, 62(1), 146-164. doi:10.1088/1361-6560/aa4f6aWilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1). doi:10.1038/sdata.2016.18Calatrava, A., Romero, E., Moltó, G., Caballer, M., & Alonso, J. M. (2016). Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Future Generation Computer Systems, 61, 13-25. doi:10.1016/j.future.2016.01.018Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5Wolstencroft, K., Owen, S., Krebs, O., Nguyen, Q., Stanford, N. J., Golebiewski, M., … Goble, C. (2015). SEEK: a systems biology data and model management platform. BMC Systems Biology, 9(1). doi:10.1186/s12918-015-0174-yDe Alfonso, C., Caballer, M., Calatrava, A., Moltó, G., & Blanquer, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing, 17(1), 191-204. doi:10.1007/s10723-018-9449-zRawla, P. (2019). Epidemiology of Prostate Cancer. World Journal of Oncology, 10(2), 63-89. doi:10.14740/wjon1191Bratan, F., Niaf, E., Melodelima, C., Chesnais, A. L., Souchon, R., Mège-Lechevallier, F., … Rouvière, O. (2013). Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study. European Radiology, 23(7), 2019-2029. doi:10.1007/s00330-013-2795-0Le, J. D., Tan, N., Shkolyar, E., Lu, D. Y., Kwan, L., Marks, L. S., … Reiter, R. E. (2015). Multifocality and Prostate Cancer Detection by Multiparametric Magnetic Resonance Imaging: Correlation with Whole-mount Histopathology. European Urology, 67(3), 569-576. doi:10.1016/j.eururo.2014.08.079Brix, G., Semmler, W., Port, R., Schad, L. R., Layer, G., & Lorenz, W. J. (1991). Pharmacokinetic Parameters in CNS Gd-DTPA Enhanced MR Imaging. Journal of Computer Assisted Tomography, 15(4), 621-628. doi:10.1097/00004728-199107000-00018Larsson, H. B. W., Stubgaard, M., Frederiksen, J. L., Jensen, M., Henriksen, O., & Paulson, O. B. (1990). Quantitation of blood-brain barrier defect by magnetic resonance imaging and gadolinium-DTPA in patients with multiple sclerosis and brain tumors. Magnetic Resonance in Medicine, 16(1), 117-131. doi:10.1002/mrm.1910160111Tofts, P. S., & Kermode, A. G. (1991). Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance in Medicine, 17(2), 357-367. doi:10.1002/mrm.1910170208Donahue, K. M., Weisskoff, R. M., & Burstein, D. (1997). Water diffusion and exchange as they influence contrast enhancement. Journal of Magnetic Resonance Imaging, 7(1), 102-110. doi:10.1002/jmri.1880070114Flouri, D., Lesnic, D., & Sourbron, S. P. (2015). Fitting the two-compartment model in DCE-MRI by linear inversion. Magnetic Resonance in Medicine, 76(3), 998-1006. doi:10.1002/mrm.25991Brun, R., & Rademakers, F. (1997). ROOT — An object oriented data analysis framework. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 389(1-2), 81-86. doi:10.1016/s0168-9002(97)00048-xXuan Liu, Comtat, C., Michel, C., Kinahan, P., Defrise, M., & Townsend, D. (2001). Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET. IEEE Transactions on Medical Imaging, 20(8), 804-814. doi:10.1109/42.938248Singh, S., Kalra, M. K., Hsieh, J., Licato, P. E., Do, S., Pien, H. H., & Blake, M. A. (2010). Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques. Radiology, 257(2), 373-383. doi:10.1148/radiol.10092212Shepp, L. A., & Vardi, Y. (1982). Maximum Likelihood Reconstruction for Emission Tomography. IEEE Transactions on Medical Imaging, 1(2), 113-122. doi:10.1109/tmi.1982.4307558Goo, J. M., Tongdee, T., Tongdee, R., Yeo, K., Hildebolt, C. F., & Bae, K. T. (2005). Volumetric Measurement of Synthetic Lung Nodules with Multi–Detector Row CT: Effect of Various Image Reconstruction Parameters and Segmentation Thresholds on Measurement Accuracy. Radiology, 235(3), 850-856. doi:10.1148/radiol.2353040737Ravenel, J. G., Leue, W. M., Nietert, P. J., Miller, J. V., Taylor, K. K., & Silvestri, G. A. (2008). Pulmonary Nodule Volume: Effects of Reconstruction Parameters on Automated Measurements—A Phantom Study. Radiology, 247(2), 400-408. doi:10.1148/radiol.2472070868Hu, Y.-H., Zhao, B., & Zhao, W. (2008). Image artifacts in digital breast tomosynthesis: Investigation of the effects of system geometry and reconstruction parameters using a linear system approach. Medical Physics, 35(12), 5242-5252. doi:10.1118/1.2996110Lyra, M., & Ploussi, A. (2011). Filtering in SPECT Image Reconstruction. International Journal of Biomedical Imaging, 2011, 1-14. doi:10.1155/2011/69379

Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies

© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The use of data Grids for sharing relevant data has proven to be successful in many research disciplines. However, the use of these environments when personal data are involved (such as in health) is reduced due to its lack of trust. There are many approaches that provide encrypted storages and key shares to prevent the access from unauthorized users. However, these approaches are additional layers that should be managed along with the authorization policies. We present in this paper a privacy-enhancing technique that uses encryption and relates to the structure of the data and their organizations, providing a natural way to propagate authorization and also a framework that fits with many use cases. The paper describes the architecture and processes, and also shows results obtained in a medical imaging platform.Manuscript received November 19, 2007; revised July 27, 2008. First published August 4,2008; cur-rent version published January 4,2009. This work was supported in part by the Spanish Ministry of Education and Science to develop the project "ngGrid-New Generation Components for the Efficient Exploitation of eScience Infrastructures," under Grant TIN2006-12860 and in part by the Structural Funds of the European Regional Development Fund (ERDF).Blanquer Espert, I.; Hernández García, V.; Segrelles Quilis, JD.; Torres Serrano, E. (2009). Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies. IEEE Transactions on Information Technology in Biomedicine. 13(1):16-24. https://doi.org/10.1109/TITB.2008.2003369S162413

Mobile devices to improve breast cancer information management

[EN] Intuitive interfaces of mobile devices facilitate the introduction of structured data in breast cancer management, leading to an increase of completeness and accuracy of diagnosis and follow-up evaluations, as well as opening the door for more effective content-based retrieval techniques for clinical decision support.Segrelles Quilis, JD.; Gimenez Fayos, M.; Blanquer Espert, I. (2013). Mobile devices to improve breast cancer information management. Ercim News. (93):45-45. http://hdl.handle.net/10251/74945S45459